Visual Distortion Detection in UGC Images Using Large Multimodal Models
Quick summary
arXiv:2608.09122v1 Announce Type: cross Abstract: The localized depiction of perceptual quality has long been a crucial, yet underexplored, challenge in image quality assessment (IQA). Existing approaches based on large multimodal models (LMMs) predominantly rely on text-driven supervised fine-tuning (SFT). However, this training paradigm exhibits notable limitations in detection accuracy. Moreover, synthetically distorted images, which are often used as the primary training data source, show a significant generalization gap when deployed in real-world scenarios; thus, the \textbf{synthetic-to
Key takeaways
- arXiv:2608.09122v1 Announce Type: cross Abstract: The localized depiction of perceptual quality has long been a crucial, yet underexplored, challenge in image quality assessment (IQA).
- Existing approaches based on large multimodal models (LMMs) predominantly rely on text-driven supervised fine-tuning (SFT).
- However, this training paradigm exhibits notable limitations in detection accuracy.
Why it matters
“Visual Distortion Detection in UGC Images Using Large Multimodal Models” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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